Division line recognition apparatus

ABSTRACT

An apparatus for recognizing a division line on a road from an image captured by a camera includes: a processing area setting unit to set a processing area to the image; a statistics calculation unit to calculate statistics of the image in the processing area; a threshold value setting unit to set a plurality of threshold values on the basis of the statistics; a division line feature point extraction unit to classify a plurality of pixels contained in the image on the basis of the white line threshold value and the road surface threshold value and extracts feature points of the division line on the basis of classification results of the plurality of pixels; and a division line decision unit configured to decide the division line on the basis of the feature points extracted by the division line feature point extraction unit.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a U.S. National Phase Patent Application and claimspriority to and the benefit of International Application NumberPCT/JP2019/007736, filed on Feb. 28, 2019, which claims priority ofJapanese Patent Application Number 2018-043554, filed on Mar. 9, 2018,the entire contents of all of which are incorporated herein byreference.

TECHNICAL FIELD

The present invention relates to an apparatus for recognizing divisionlines on roads.

BACKGROUND ART

Conventionally, there has been known a technology that recognizesdivision lines (white lines) on a road from a captured image(s) of theroad and utilizes them for, for example, controlling vehicles. When adivision line is recognized by directly using a luminance value of thecaptured image, feature points of edges sometimes appear not only atboundary portions of the division line, but also within the divisionline. So, there has been a problem of difficulty in determining whichfeature points indicate the boundary of the division line. Furthermore,when the luminance value of the division line decreases due to theinfluence of, for example, shadows of vehicles or the luminance value ofa road surface rises due to a reflection of strong light on the roadsurface, there has also been a problem of difficulty in distinguishingbetween the division line and the road surface.

The following PTL 1 is known as a conventional technology to solve theabove-described problems. PTL 1 describes a lane detection apparatusincluding: an imaging apparatus that captures images of an area in frontof a vehicle including a road surface; and a lane mark detection unitthat detects a lane mark formed on the road surface by executingbinarization processing on an image captured by the imaging apparatus byusing a binarization threshold value, wherein the lane mark detectionunit: divides the captured image into a plurality of blocks whichcontinue in a vertical direction and extend in a crosswise direction;sorts the plurality of blocks into bright part blocks with relativelyhigh luminance values and dark part blocks with relatively low luminancevalues on the basis of the luminance values of the divided blocks; andexecutes the binarization processing on the captured image by changingthe binarization threshold value for the bright part blocks and for thedark part blocks.

CITATION LIST Patent Literature

PTL 1: Japanese Patent Application Laid-Open (Kokai) Publication No.2016-206881

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

With the technology described in PTL 1, it is difficult to determinedivision lines appropriately under conditions where it is impossible todistinguish a luminance value of the division line from that of the roadsurface locally. Therefore, there is room for improvement of theaccuracy in recognizing the division lines on the road.

Means to Solve the Problems

A division line recognition apparatus according to the present inventionis an apparatus for recognizing a division line on a road from an imagecaptured by a camera, wherein the division line recognition apparatusincludes: a processing area setting unit configured to set a processingarea to the image; a statistics calculation unit configured to calculatestatistics of the image in the processing area; a threshold valuesetting unit configured to set a plurality of threshold values on thebasis of the statistics; a division line feature point extraction unitconfigured to classify a plurality of pixels contained in the image onthe basis of the plurality of threshold values to distinguish betweenthe road surface and a white line and extracts feature points of thedivision line on the basis of classification results of the plurality ofpixels; and a division line decision unit configured to decide thedivision line on the basis of the feature points extracted by thedivision line feature point extraction unit.

Advantageous Effects of the Invention

According to the present invention, accuracy in recognizing the divisionlines on the road can be improved.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a hardware configurationof a division line recognition apparatus according to one embodiment ofthe present invention;

FIG. 2 is a diagram illustrating an example of a functionalconfiguration of the division line recognition apparatus according toone embodiment of the present invention;

FIG. 3 is a diagram illustrating a processing flow of a division linerecognition apparatus according to a first embodiment of the presentinvention;

FIG. 4 is a diagram illustrating an example of a threshold value settingmethod by a threshold value setting unit;

FIG. 5 is a diagram illustrating a processing flow of a division linefeature point extraction unit;

FIG. 6 is a diagram illustrating a processing flow of a classificationunit;

FIG. 7 is a diagram illustrating a processing flow of a feature pointdecision unit;

FIG. 8 is a diagram illustrating an example of a data structure of afeature point candidate buffer;

FIG. 9 is a diagram illustrating an example of a data structure of afeature point buffer;

FIG. 10 is a diagram illustrating an example of an image beforeclassification;

FIG. 11 is a diagram illustrating an example of a classified image;

FIG. 12 is a diagram for explaining an example of extracting rise pointsand fall points;

FIG. 13 is a diagram for explaining an example of the result ofextracting the rise points and the fall points;

FIG. 14 is a diagram illustrating an example of a feature point decisionresult in the entire processing area;

FIG. 15 is a diagram illustrating a processing flow of a division linerecognition apparatus according to a second embodiment of the presentinvention;

FIG. 16 is a diagram illustrating an example of a threshold valuesetting method by a threshold value setting unit; and

FIG. 17 is a diagram illustrating an example of an image beforeclassification.

DESCRIPTION OF EMBODIMENTS First Embodiment

FIG. 1 is a diagram illustrating an example of a hardware configurationof a division line recognition apparatus according to one embodiment ofthe present invention. A division line recognition apparatus 1illustrated in FIG. 1: is designed to recognize division lines on a roadfrom an image(s) captured by a camera as it is mounted in a vehicle andused; and includes a storage apparatus 103, a CPU 104, a memory 105, anda CAN I/F 106. The following cameras are coupled to the division linerecognition apparatus 1: a front camera 101-Fr for monitoring an area infront of the vehicle; a rear camera 101-Rr for monitoring an area behindthe vehicle; a left-side camera 101-SL for monitoring an area on theleft side of the vehicle; a right-side camera 101-SR for monitoring anarea on the right side of the vehicle. Furthermore, a vehicle controlunit 108 is coupled to the division line recognition apparatus 1 via aCAN bus 107. Incidentally, in an example in FIG. 1, the above-describedfour cameras are coupled to the division line recognition apparatus 1;however, as images captured by each of an arbitrary number of camerasare input to the division line recognition apparatus 1, the divisionline recognition apparatus 1 can recognize the division line on the roadfrom each image. Therefore, the following explanation will be providedby referring to one camera 101 without distinguishing theabove-mentioned four cameras from each other.

The storage apparatus 103 stores various kinds of information andprograms which are required for operations of the division linerecognition apparatus 1. The CPU 104 executes a specified program storedin the storage apparatus 103 and thereby executes imaging processing byusing an image(s) input from the camera 101 and recognizes a divisionline(s) on the road existing around the vehicle. The memory 105 is avolatile storage medium and is used as a work area by the CPU 104 whenexecuting the imaging processing, and accumulates division linerecognition results obtained by the imaging processing. Information ofthe division line recognition results accumulated in the memory 105 isoutput via the CAN I/F 106 to the CAN bus 107 and transmitted to thevehicle control unit 108. The vehicle control unit 108 performs variouskinds of vehicle control according to a running state of the vehicle,for example, to control steering of the vehicle to prevent the vehiclefrom deviating from a driving lane by using the division linerecognition results transmitted from the division line recognitionapparatus 1.

FIG. 2 is a diagram illustrating an example of a functionalconfiguration of the division line recognition apparatus according toone embodiment of the present invention. The division line recognitionapparatus 1 illustrated in FIG. 2 functionally includes a processingarea setting unit 201, a statistics calculation unit 202, a thresholdvalue setting unit 203, a division line feature point extraction unit205, and a division line decision unit 206. The threshold value settingunit 203 is configured of a white line threshold value setting unit203-1 and a road surface threshold value setting unit 203-2. Thedivision line feature point extraction unit 205 is configured of aclassification unit 205-1, a feature point candidate extraction unit205-2, and a feature point decision unit 205-3. The division linerecognition apparatus 1 implements each of these functional blocks byhaving the CPU 104 execute a specified program.

Images captured by the camera 101 are transmitted at specified timeintervals to the processing area setting unit 201. Each of the imageswhich are input at specified time intervals may hereinafter sometimes bereferred to as a “frame.” The processing area setting unit 201 sets aprocessing area to an image which has been input from the camera 101.The processing area which is set here means an image area used toextract a division line (or white line) on the road from the image.Therefore, the processing area is set around a place where it is assumedthat a white line exists; and if the white line was recognized in aprevious frame, the processing area can be set with reference to thatposition. For example, in a case of the right-side camera 101-SR or theleft-side camera 101-SL, the processing area can be set by setting apredefined margin above and under the white-line-recognized position inthe previous frame. If the processing area is not detected in theprevious frame, a predefined area may be used. The purpose of settingthe processing area in the image is to shorten processing time bynarrowing down the area on which the processing is to be executedbecause it takes time to process the entire image if the processingcapacity of the CPU 104 is not sufficient.

However, if the processing capacity of the CPU 104 is sufficient, theentire image may be set as the processing area. Incidentally, thefollowing explanation will be provided by assuming that a division lineis a white line; however, the same applies to a division line other thanthe white line.

The processing area setting unit 201 sets the processing area in acurrent frame on the basis of, for example, the position of a white linein a previous frame, that is, the position of the white line recognizedfrom the image which was a processing object last time. Incidentally, ifthe white line was not successfully recognized in the previous frame,the processing area may be set on the basis of the position of a whiteline in a further preceding frame or a default processing area may beset to a predetermined position. Accordingly, it is possible to preventlengthening of the processing time due to a failure to set theprocessing area. Furthermore, if the white line is a broken line, theprocessing area can be set on the basis of the position of the whiteline in a frame where the white line was recognized last time.

The statistics calculation unit 202 calculates the statistics of theimage in the processing area which is set by the processing area settingunit 201. Specifically speaking, for example, an average value and astandard deviation of the luminance value of each pixel of the image inthe processing area are calculated as the statistics.

The threshold value setting unit 203 causes the white line thresholdvalue setting unit 203-1 and the road surface threshold value settingunit 203-2 to set a white line threshold value and a road surfacethreshold value, respectively, on the basis of the statistics of theimage, which are found by the statistics calculation unit 202, that is,the average value and the standard deviation of the luminance value ofeach pixel in the processing area. The white line threshold value is athreshold value for recognizing a white line part from the image in theprocessing area and corresponds to a minimum luminance value of a pixelbelonging to the white line. Specifically, a pixel having the luminancevalue which is equal to or larger than the white line threshold value isextracted as a white line candidate. Furthermore, the road surfacethreshold value is a threshold value for recognizing a road surface partfrom the image in the processing area and corresponds to a maximumluminance value of a pixel belonging to the road surface other than thewhite line. Specifically, a pixel having the luminance value which isequal to or smaller than the road surface threshold value is extractedas the road surface candidate. Incidentally, in the followingexplanation, parts other than the white line on the road will becollectively referred to as the “road surface.” A specific method ofsetting the white line threshold value and the road surface thresholdvalue will be explained later.

The division line feature point extraction unit 205 causes theclassification unit 205-1, the feature point candidate extraction unit205-2, and the feature point decision unit 205-3 to extract featurepoints of the white line from the image on the basis of the white linethreshold value and the road surface threshold value which are set bythe threshold value setting unit 203. The classification unit 205-1classifies each pixel contained in the image within the processing areainto any one of the “road surface,” the “white line,” and “indefinite”by using the white line threshold value and the road surface thresholdvalue. Specifically, when the luminance value of each pixel is comparedwith the white line threshold value and the road surface threshold valueand whether the relevant pixel belongs to either the “white line” or the“road surface” is judged and if it is determined that the relevant pixelbelongs neither of them, the relevant pixel is classified as“indefinite.” Incidentally, a well-known edge extraction method such asa differential filter like a Sobel filter may be used to extract whiteline edges from the image within the processing area and theclassification unit 205-1 may set pixels existing around such white lineedges as objects and classify each of the pixels. In this case, pixelswhich are not classified will be excluded from objects of subsequentprocessing.

The feature point candidate extraction unit 205-2 extracts feature pointcandidates which are candidates for feature points indicating theoutline of the white line on the basis of the classification results ofthe respective pixels by the classification unit 205-1. Specificallyspeaking, the feature point candidate extraction unit 205-2: finds aboundary at which the classification of the pixels changes; and extractspoints corresponding to that boundary as the feature point candidates.

The feature point decision unit 205-3 decides the feature points fromthe feature point candidates on the basis of the positional relationshipbetween the plurality of feature point candidates extracted by thefeature point candidate extraction unit 205-2. Specifically speaking,the feature point decision unit 205-3 determines which combination ofthe feature point candidates would be certain as the outline of thewhite line by referring to division line standard data 204 stored in thestorage apparatus 103 and checking the distance between the plurality ofextracted feature point candidates against the division line standarddata 204. Incidentally, the division line standard data 204: is adatabase which stores standard values such as the width and length ofthe white line; and is stored in advance in the storage apparatus 103.

The division line decision unit 206 recognizes the white line drawn onthe road on the basis of the feature points extracted by the divisionline feature point extraction unit 205 and determines the position ofthe white line in the image. For example, the division line decisionunit 206 performs coordinate transformation of all the feature pointswithin the processing area into a specified coordinate system, in whichdistortion of the image is corrected, and then recognizes the white lineby using a method of, for example, trimetric projection. Specificallyspeaking, while changing an angle of the coordinate axis by a certainwidth, each feature point which has been coordinate-transformed isprojected onto the x-coordinate axis and the y-coordinate axis,respectively, and accumulated values of edge intensity are aggregated.Then, the white line can be recognized by finding the angle and positionwhich make the edge intensity maximum. Incidentally, since thecoordinate transformation of the feature points requires cameraparameters such as the angle of view and an image capturing direction ofthe camera 101, it is desirable that the camera parameters for thecoordinate transformation should be stored in, for example, the storageapparatus 103 in advance. When the white line is recognized in this way,the division line decision unit 206 may judge the type of a traffic signrepresented by the white line by identifying whether the white line is asolid line or a broken line.

Next, a processing flow of the division line recognition apparatus 1will be explained. FIG. 3 is a diagram illustrating a processing flow ofthe division line recognition apparatus according to a first embodimentof the present invention. The processing flow in FIG. 3 illustrates theprocessing of one-frame image. Specifically, with the division linerecognition apparatus 1 according to this embodiment, the CPU 104executes processing for recognizing a division line from an image inaccordance with the processing flow in FIG. 3 every time the image isinput from the camera 101 at a specified frame rate.

In step 301, a camera image, that is, an image which is input from thecamera 101 is acquired.

In step 302, the processing area setting unit 201 is caused to set theprocessing area in the image acquired in step 301. Under thiscircumstance, the position of the processing area within the image isdecided on the basis of, for example, the position of the white line inthe previous frame as described earlier.

In step 303, the statistics calculation unit 202 is caused to find thestatistics of the luminance value in the processing area which was setin step 302. Under this circumstance, the average value and the standarddeviation of the luminance value of each pixel in the processing areaare calculated as the statistics of the image as described earlier.

In step 304, the threshold value setting unit 203 is caused to setthreshold values for the luminance value on the basis of the statisticsfound in step 303. Under this circumstance, the white line thresholdvalue setting unit 203-1 is caused to set the white line threshold valueand the road surface threshold value setting unit 203-2 is caused to setthe road surface threshold value as described earlier. Incidentally, thedetails of a method for setting the threshold values in step 304 will beexplained later with reference to FIG. 4 and FIG. 16.

In step 305, the division line feature point extraction unit 205 iscaused to extract feature points of the division line, that is, thewhite line from the image within the processing area, which was set instep 302, on the basis of the threshold values set in step 304.Incidentally, the details of a method for extracting the feature pointsin step 305 will be explained later with reference to FIG. 5 to FIG. 9.

In step 306, the division line decision unit 206 is caused to decide thedivision line (the white line) on the road from the feature pointsextracted in step 305. After executing step 306, the CPU 104 terminatesthe processing flow in FIG. 3.

Next, the details of the method for setting the threshold values in step304 will be explained. FIG. 4 is a diagram illustrating an example ofthe threshold value setting method by the threshold value setting unit203.

Let us assume that an average value p and a standard deviation a arecalculated as the statistics of the luminance value of each pixel in theprocessing area by the statistics calculation unit 202. In this case,the road surface threshold value setting unit 203-2 sets, for example,the average value p as a road surface threshold value 404 as illustratedin FIG. 4. Accordingly, the division line feature point extraction unit205 is caused to classify each pixel contained in an area 401 of asmaller luminance value than the road surface threshold value 404 intothe “road surface.” On the other hand, the white line threshold valuesetting unit 203-1 sets, for example, p+Q which is obtained by addingthe standard deviation a to the average value p, as a white linethreshold value 405 as illustrated in FIG. 4. Accordingly, the divisionline feature point extraction unit 205 is caused to classify each pixelcontained in an area 403 of a larger luminance value than the white linethreshold value 405 into the “white line.” Incidentally, a pixel whichis not classified into either the “road surface” or the “white line,”that is, each pixel contained in an area 402 corresponding to theluminance value which is equal to or larger than the road surfacethreshold value 404 and equal to and smaller than the white linethreshold value 405 is classified into “indefinite” by the division linefeature point extraction unit 205.

The method in FIG. 4 is established on the premise that the luminancevalue is conformable to normal distribution; however, it may possiblynot be true in many cases. In such cases, it is effective to use thestatistics such as a median value or a median absolute deviation whichmay hardly be affected by any bias in distribution even if there is suchbias in distribution.

FIG. 16 is a diagram illustrating another example of the threshold valuesetting method by the threshold value setting unit 203. Let us assumethat a median value M and a median absolute deviation MAD are calculatedas the statistics for the luminance value of each pixel in theprocessing area by the statistics calculation unit 202. In this case,the road surface threshold value setting unit 203-2 sets, for example,the median value M as a road surface threshold value 1604 as illustratedin FIG. 16. Accordingly, the division line feature point extraction unit205 is caused to classify each pixel contained in an area 1601 of asmaller luminance value than the road surface threshold value 1604 intothe “road surface.” On the other hand, the white line threshold valuesetting unit 203-1 sets, for example, M+MAD which is obtained by addingthe median absolute deviation MAD to the median value M, as a white linethreshold value 1605 as illustrated in FIG. 16. Accordingly, thedivision line feature point extraction unit 205 is caused to classifyeach pixel contained in an area 1603 of a larger luminance value thanthe white line threshold value 1605 into the “white line.” Incidentally,a pixel which is not classified into either the “road surface” or the“white line,” that is, each pixel contained in an area 1602corresponding to the luminance value which is equal to or larger thanthe road surface threshold value 1604 and equal to and smaller than thewhite line threshold value 1605 is classified into “indefinite” by thedivision line feature point extraction unit 205.

By setting the white line threshold value and the road surface thresholdvalue, respectively, as described above, each pixel whose luminancevalue is relatively high in the processing area can be extracted as thewhite line candidate. Furthermore, by using the statistics, even ifbrightness of the frame changes due to any change in the surroundingenvironment of the vehicle, it is possible to set the white linethreshold value and the road surface threshold value and extract thewhite line candidates without being impacted by the above-describedchange. Incidentally, the method for setting the white line thresholdvalue and the road surface threshold value is not limited to thosemethods described above. Any arbitrary method can be used as long as thewhite line threshold value and the road surface threshold value can beset respectively appropriately on the basis of the statistics of theluminance value of each pixel in the processing area.

Furthermore, the road surface threshold value 404 and the white linethreshold value 405 can be set by dividing the processing area into anappropriate number of areas and finding the statistics for each of thedivided areas instead of finding the statics by targeting the entireprocessing area. For example, more appropriate threshold values can beset by dividing the processing into three areas as illustrated in FIG.17 and setting the statistics and the threshold values for each of thedivided areas.

Next, the details of the feature point extraction method in step 305 inFIG. 3 will be explained. FIG. 5 is a diagram illustrating a processingflow of the division line feature point extraction unit 205 in step 305.

In step 501, the classification unit 205-1 is caused to judge theclassification of each pixel in the processing area. Under thiscircumstance, as the classification unit 205-1 executes theclassification processing illustrated in the processing flow in FIG. 6,each pixel in the processing area is classified as any one of the “roadsurface,” the “white line,” and “indefinite.” Incidentally, the detailsof the processing flow in FIG. 6 will be explained later.

In steps 502 to 506, the feature point candidate extraction unit 205-2is caused to execute the following processing with respect to each pixelin the processing area by using the classification results of step 501.Under this circumstance, the X-axis is defined as a left-to-rightdirection of the image and the Y-axis is defined as an up-to-downdirection.

In step 502, regarding pixels which are processing objects, a pair ofpixels regarding which their x-coordinates are the same and thedifference between their y-coordinates is 1 is selected. Incidentally,this is a case where the camera 101 is the left-side camera 101-SL orthe right-side camera 101-SR. On the other hand, if the camera 101 isthe front camera 101-Fr or the rear camera 101-Rr, an extendingdirection of the division line in the image changes by approximately 90degrees; and, therefore, it is desirable that a pair of pixels regardingwhich their y-coordinates are the same and the difference between theirx-coordinates is 1 should be selected. Accordingly, processing similarto that of the case of the left-side camera 101-SL or the right-sidecamera 101-SR can be executed.

In step 503, the classification results in step 501 are compared witheach other regarding the pair of pixels selected in step 502. As aresult, when scanning is performed in a direction so that thex-coordinate stays the same and the y-coordinate increases, and if theclassification of the pixels changes from “indefinite” to the “whiteline” or from the “road surface” to the “white line,” or from the “roadsurface” to “indefinite,” one of the pixels, for example, the pixelwhose y-coordinate value is larger is recognized as a rise point and isbuffered in step 504. Incidentally, the rise point: is a point where theluminance value changes in an increasing direction; and corresponds to afeature point candidate indicating the outline of the white line. Afterthe relevant pixel is buffered as the rise point, the processing returnsto step 502 and proceeds to the processing of the next pixel. On theother hand, if the above-described condition is not satisfied, theprocessing proceeds to step 505.

In step 505, the classification results in step 502 are compared witheach other with respect to the pair of pixels selected in step 502 in amanner similar to step 503. As a result, when scanning is performed in adirection so that the x-coordinate stays the same and the y-coordinateincreases, and if the classification of the pixels changes from“indefinite” to the “road surface,” or from the “white line” to the“road surface,” or from the “white line” to “indefinite,” one of thepixels, for example, the pixel whose y-coordinate value is larger isrecognized as a fall point and is buffered in step 506. Incidentally,the fall point: is a point where the luminance value changes in adecreasing direction; and corresponds to a feature point candidateindicating the outline of the white line. After the relevant pixel isbuffered as the fall point, the processing returns to step 502 andproceeds to the processing of the next pixel. On the other hand, if theabove-described condition is not satisfied, the processing proceeds tostep 502 without doing anything and then proceeds to the processing ofthe next pixel.

In steps 502 to 506, whether the relevant pixel is the rise point or thefall point, which is the feature point candidate, or whether therelevant pixel is neither the rise point nor the fall point is judgedwith respect to all the pixels in the processing area by executing theabove-described processing on each pixel in the processing area. Afterexecuting the processing in steps 502 to 506 with respect to all thepixels in the processing area, the processing proceeds to step 507.

In step 507, the feature point decision unit 205-3 is caused to checkthe positions of the respective feature point candidates judged in steps502 to 506, that is, the positions of the rise point and the fall pointand decide the feature points. Under this circumstance, as the featurepoint decision unit 205-3 executes the feature point decision processingillustrated in the processing flow in FIG. 7, the feature points aredecided from the feature point candidates on the basis of the positionalrelationship between the plurality of feature point candidates extractedfrom the image in the processing area. Incidentally, the details of theprocessing flow in FIG. 7 will be explained later. After executing theprocessing in step 507, the processing in step 305 in FIG. 3 isterminated and proceeds to the next step 306.

FIG. 6 is a diagram illustrating the processing flow of theclassification unit 205-1 in step 501 in FIG. 5. The classification unit205-1 executes processing in each of steps 601 to 606 explained belowwith respect to each pixel in the processing area.

In step 601, the luminance value of a pixel which is the processingobject in the image is acquired.

In step 602, the luminance value acquired in step 601 is compared withthe road surface threshold value which is set by the road surfacethreshold value setting unit 203-2 in step 304 in FIG. 3. As a result,if the luminance value is smaller than the road surface threshold value,the relevant pixel is classified as the “road surface” in step 604 andthe processing proceeds to the next pixel. On the other hand, if theluminance value is equal to or larger than the road surface thresholdvalue, the processing proceeds to step 603.

In step 603, the luminance value acquired in step 601 is compared withthe white line threshold value which is set by the white line thresholdvalue setting unit 203-1 in step 304 in FIG. 3. As a result, if theluminance value is larger than the white line threshold value, therelevant pixel is classified as the “white line” in step 605 and theprocessing proceeds to the processing of the next pixel. On the otherhand, if the luminance value is equal to or smaller than the white linethreshold value, the relevant pixel is classified as “indefinite” instep 606 and the processing proceeds to the processing of the nextpixel.

In steps 601 to 606, all the pixels in the processing area areclassified into any one of the “road surface,” the “white line,” and“indefinite” by executing the above-described processing on each pixelin the processing area. After executing the processing in steps 601 to606 with respect to all the pixels in the processing area, theprocessing in step 501 in FIG. 5 is terminated and proceeds to the nextstep 502.

FIG. 7 is a diagram illustrating the processing flow of the featurepoint decision unit 205-3 in step 507 in FIG. 5.

In step 701, lane width data, that is, data representing the width ofthe white line is acquired from the storage apparatus 103. Under thiscircumstance, for example, the lane width data which is stored as partof the division line standard data 204 in the storage apparatus 103 isacquired. When this happens, the type of the road where the vehicle iscurrently driving may be acquired from, for example, a navigationapparatus which is not illustrated in the drawing and is coupled to thedivision line recognition apparatus 1 via the CAN bus 107; and the lanewidth data corresponding to that road type may be searched and acquiredfrom the division line standard data 204. Incidentally, if the divisionline recognition apparatus 1 also serves as the navigation apparatus,the lane width data corresponding to the type of the road where thevehicle is driving can be acquired in the same manner as describedabove.

In step 702, an initial value of a lane width error minimum value isset. The lane width error minimum value: is used in the subsequentprocessing to judge whether the positional relationship between the risepoint and the fall point is certain as feature points or not; is storedin the memory 105; and is updated according to the processing statusfrom time to time. Under this circumstance, for example, 20 cm is set asthe initial value of the lane width error minimum value. However, 20 cmis one example; and any arbitrary numerical value which is equal to orwider than the width of an actual white line can be set as the initialvalue of the lane width error minimum value.

In steps 703 to 707, the following processing is executed on each of thefeature point candidates extracted from the image in the processing areaby the feature point candidate extraction unit 205-2 in steps 502 to 506in FIG. 5, that is, on each of the rise points and the fall points whichare buffered in steps 504, 506.

In step 703, regarding the rise point which is the processing object andthe fall point whose x-coordinate is the same as that of theabove-mentioned rise point, the distance between these points iscalculated. Specifically, a pair of the rise point and the fall pointregarding which their x-coordinates are the same and the differencebetween their y-coordinates is 1 is selected from the plurality offeature point candidates extracted from the image in the processing areaand the distance between them according their positional relationship isfound.

In step 704, the distance found in step 703 is compared with the widthof the white line represented by the lane width data acquired in step701, an error (difference) between them is calculated.

In step 705, the error found in step 704 is compared with the currentlane width error minimum value. As a result, if the error is smallerthan the lane width error minimum value, the lane width error minimumvalue is replaced with the relevant error in step 706 and then featurepoint information is updated in step 707 by deciding the relevant fallpoint as a feature point. Incidentally, the feature point information isinformation indicating the feature point decided by the feature pointdecision unit 205-3 and is recorded so that the x-coordinates do notoverlap in the feature point buffer provided in the memory 105.Specifically, even if a plurality of fall points whose x-coordinates arethe same exist, only one fall point at a maximum is recorded as thefeature point in the feature point information.

However, if the condition in step 705 is not satisfied with respect toany one of the fall points, it is considered that no feature pointcorresponding to the relevant x-coordinate exists; and no feature pointis recorded in the feature point information. After updating the featurepoint information in step 707, the processing returns to step 703 andproceeds to the processing on the next combination of a rise point and afall point. On the other hand, if it is determined in step 705 that theerror is equal to and larger than the lane width error minimum value,the processing returns to step 502 without doing anything, and thenproceeds to the processing on the next combination of a rise point and afall point.

In steps 703 to 707, the positional relationship between the featurepoint candidates is checked against each other with respect to all thefeature point candidates extracted from the processing area and theirconsistency as the outline of the white line is checked by executing theabove-described processing on each combination of the rise point and thefall point. As a result, the feature points can be decided from thefeature point candidates by identifying a combination of the rise pointand the fall point, which is closest to the width of the white line inthe processing area, with respect to each x-coordinate and retaininginformation of that fall point as the feature point information.Incidentally, in the example in FIG. 7, the fall point is recorded asthe feature point information; however, the rise point may be recordedas the feature point information or both the rise point and the fallpoint may be recorded as the feature point information. After executingthe processing in steps 703 to 707 with respect to all the rise pointsand the fall points which are extracted in the processing area, theprocessing in step 507 in FIG. 5 is terminated and the processing flowof the division line feature point extraction unit 205 is completed.

FIG. 8 is a diagram illustrating an example of a data structure of afeature point candidate buffer which is used when buffering each of therise points and the fall points in steps 504 and 506 in FIG. 5. Thefeature point candidate buffer illustrated in FIG. 8 is implemented byusing the memory 105 and stores each piece of data of a rise pointquantity 801, rise point information 802, a fall point quantity 804, andfall point information 805. The rise point quantity 801 is dataindicating the quantity of the rise points and is equivalent to the dataquantity of the rise point information 802. Specifically, the samequantity of the rise point information 802 as the quantity indicated bythe rise point quantity 801 is stored in the feature point candidatebuffer. The rise point information 802 is data indicating the positionof the rise point in the image and is composed of an x-coordinate 802-1and a y-coordinate 802-2 which represent an x-coordinate value and ay-coordinate value, respectively, of the rise point in the imagecoordinate system. Similarly regarding the fall point, the fall pointquantity 804 is data indicating the quantity of the fall points and isequivalent to the data quantity of the fall point information 805.Specifically, the same quantity of the fall point information 805 as thequantity indicated by the fall point quantity 804 is stored in thefeature point candidate buffer. The fall point information 805 is dataindicating the position of the fall point in the image and is composedof an x-coordinate 805-1 and a y-coordinate 805-2 which represent anx-coordinate value and a y-coordinate value, respectively, of the fallpoint in the image coordinate system.

FIG. 9 is a diagram illustrating an example of a data structure of afeature point buffer which is used when buffering the feature pointinformation in step 707 in FIG. 7. The feature point buffer illustratedin FIG. 9 is implemented by using the memory 105 and stores each pieceof data of a feature point quantity 901 and feature point information902. The feature point quantity 901 is data indicating the quantity ofthe feature points and is equivalent to the data quantity of the featurepoint information 902. Specifically, the same quantity of the featurepoint information 902 as the quantity indicated by the feature pointquantity 901 is stored in the feature point buffer. The feature pointinformation 902 is data indicating the position and classification ofthe feature point in the image and is composed of an x-coordinate 902-1and a y-coordinate 902-2 which represent an x-coordinate value and ay-coordinate value, respectively, of the feature point in the imagecoordinate system, and classification 902-3.

Next, a specific example of the above-described processing will beexplained with reference to FIG. 10 to FIG. 14. Incidentally, in thefollowing explanation, an explanation will be provided about a specificexample of a case where the processing is executed on an image, as anobject, which is captured by the right-side camera 101-SR; however, thesame applies to images captured by other cameras.

FIG. 10 is a diagram illustrating an example of an image beforeclassification. The processing area setting unit 201 sets a processingarea 1002 to an image 1001 input from the camera 101, for example,within the range surrounded by a white frame in the drawing. A whiteline which is the object to be recognized is included in the processingarea 1002 and shadows of the grass are partly over the white line.Incidentally, the processing area 1002 is determined, for example, onthe basis of the position of the white line in the previous frame asexplained earlier.

The statistics calculation unit 202 calculates the statistics of theluminance value of each pixel within the processing area 1002 which isset to the image 1001. The threshold value setting unit 203 sets thewhite line threshold value and the road surface threshold value on thebasis of the statistics calculated by the statistics calculation unit202. The classification unit 205-1 classifies each pixel of the image1001 into any one of the “road surface,” the “white line,” and“indefinite” by using the white line threshold value and the roadsurface threshold value which are set by the threshold value settingunit 203.

FIG. 11 is a diagram illustrating an example of a classified image 1101obtained by classifying each pixel of the image 1001 in FIG. 10. Withthe classified image 1101, the classification results of the respectivepixels are indicated as the following three types: black, white, andgray. The black corresponds to the classification result of the “roadsurface,” the white corresponds to the classification result of the“white line,” and the gray corresponds to the classification result of“indefinite,” respectively. Under this circumstance, it can be seen thatregarding the respective pixels corresponding to the white line in theimage 1001 in FIG. 10, most of such pixels are classified as the “whiteline” in the classified image 1101 in FIG. 11, but some pixels arepartly classified as “indefinite” because the shadows of the grass arelaid over them. Furthermore, it can be seen that regarding therespective pixels corresponding to the road surface in the image 1001 inFIG. 10, many parts of such pixels are classified as “indefinite” in theclassified image 1101 in FIG. 11. Incidentally, the classificationresults of the entire area of the image 1001 are indicated as theclassified image 1101 for the purpose of easy comprehension in FIG. 11;however, practically, only the pixels in the processing area 1002 may beclassified as explained earlier.

The feature point candidate extraction unit 205-2 extracts the risepoints and the fall points which are the feature point candidates fromthe classified image 1101 on the basis of the classification results ofthe respective pixels indicated in the classified image 1101.

FIG. 12 is a diagram for explaining an example of rise points and fallpoints extracted by the feature point candidate extraction unit 205-2.Referring to FIG. 12, part of the portion corresponding to theprocessing area 1002 in the classified image 1101 is taken out andshown. The feature point candidate extraction unit 205-2: sequentiallyselects a pair of pixels regarding which their x-coordinates are thesame and the difference between their y-coordinates is 1 as explainedearlier; and extracts a point(s) where the classification resultchanges, as a rise point or a fall point. Accordingly, for example, whena point(s) at which the classification result changes is searched foralong a scanning direction 1200 with respect to each pixel whosex-coordinate value is x1 as illustrated in FIG. 12, a rise point 1201, afall point 1202, and a fall point 1203 are extracted. The rise point1201 is the point where its classification changes from the “roadsurface” to the “white line.” The fall point 1202 is the point where itsclassification changes from the “white line” to “indefinite.” The fallpoint 1203 is the point where its classification changes from“indefinite” to the “road surface.” Incidentally, the fall point 1202corresponds to a shadow part on the white line. Accordingly, the fallpoint may sometimes be extracted not only at the boundary between thewhite line and the road surface, but also the shadow part on the whiteline. The rise points and the fall points which are extracted in thisway by scanning to the lower end of the processing area 1002, that is,to the point where the y-coordinate becomes maximum are buffered as thefeature point candidates, respectively.

After the above-described processing for extracting the rise points andthe fall points with respect to the entire processing area 1002 in theclassified image 1101 is terminated, the result illustrated in FIG. 13is obtained. FIG. 13 is a diagram for explaining an example of theresult of extracting the rise points and the fall points. In FIG. 13,the rise points and the fall points which are extracted within theprocessing area 1002 in the classified image 1101 are represented by therespective black points represented by a point indicated with thereference numeral 1301.

The feature point decision unit 205-3 checks the positional relationshipbetween the respective rise points and fall points which are extractedas the feature point candidates from the classified image 1101 againstthe division line standard data 204 and decides feature points fromamong these points. For example, regarding the rise point 1201, the fallpoint 1202, and the fall point 1203 illustrated in FIG. 12, thepositional relationship between the rise point 1201 and the fall point1202 and the positional relationship between the rise point 1201 and thefall point 1203 are respectively checked against the width (orthickness) of the white line indicated by the division line standarddata 204. As a result, the distance between the rise point 1201 and thefall point 1202 does not make sense as the width of the white line, sothat this combination is judged as inappropriate as representing theoutline of the white line and information of the fall point 1202 is notrecorded in the feature point information. On the other hand, thedistance between the rise point 1201 and the fall point 1203substantially matches the width of the white line, so that thiscombination is judged as appropriate as representing the outline of thewhite line and information of the fall point 1203 is recorded in thefeature point information. Incidentally, under this circumstance,information of the fall point positioned more closer to the driver's ownvehicle, in the combination of the rise point and the fall point whichis judged as appropriate, is recorded in the feature point information;however, as explained earlier, information of the rise point may berecorded in the feature point information or information of both therise point and the fall point may be recorded in the feature pointinformation.

FIG. 14 is a diagram illustrating an example of the feature pointdecision result in the entire processing area 1002 in the classifiedimage 1101. In FIG. 14, each feature point decided by a fall pointregarding which the distance between that fall point and itscorresponding rise point substantially matches the width of the whiteline is represented by each black point represented by a point indicatedwith the reference numeral 1401.

According to the above-described first embodiment of the presentinvention, the following operational advantages are obtained.

(1) The division line recognition apparatus 1 is an apparatus forrecognizing a division line on a road from an image captured by thecamera 101. The division line recognition apparatus 1 includes: theprocessing area setting unit 201 that sets a processing area to theimage; the statistics calculation unit 202 that calculates statistics ofthe image in the processing area; the threshold value setting unit 203that sets a plurality of threshold values, that is, the white linethreshold value and the road surface threshold value on the basis of thestatistics; the division line feature point extraction unit 205 thatclassifies a plurality of pixels contained in the image on the basis ofthe white line threshold value and the road surface threshold value andextracts feature points of the division line on the basis ofclassification results of the plurality of pixels; and a division linedecision unit 206 that decides the division line on the basis of thefeature points extracted by the division line feature point extractionunit 205. Consequently, the accuracy in recognizing the division line onthe road can be improved.

(2) The statistics calculation unit 202 calculates the average value andthe standard deviation of the luminance value of each pixel of the imagein the processing area as the statistics (step 303 in FIG. 3).Consequently, it is possible to calculate the statistics which arerequired to set the white line threshold value and the road surfacethreshold value appropriately.

(3) The division line feature point extraction unit 205 causes thefeature point candidate extraction unit 205-2 to extract pointscorresponding to the boundary at which the classification of theplurality of pixels contained in the image changes, as feature pointcandidates (steps 502 to 506 in FIG. 5). Consequently, appropriatepoints can be extracted as the feature point candidates by using theclassification results of the pixels by the classification unit 205-1.

(4) The division line feature point extraction unit 205 causes theclassification unit 205-1 to classify the plurality of pixels containedin the image into any one of at least three types, that is, the “roadsurface,” the “white line,” and “indefinite” (steps 601 to 606 in FIG.6). Consequently, it is possible to perform the classification suited toextract the feature point candidates from the plurality of pixelscontained in the image by using the plurality of threshold values whichare set by the threshold value setting unit 203.

(5) The division line feature point extraction unit 205 causes thefeature point candidate extraction unit 205-2 to extract the pluralityof feature point candidates on the basis of the classification resultsof the plurality of pixels contained in the image (steps 502 to 506 inFIG. 5) and causes the feature point decision unit 205-3 to decide thefeature points from the feature point candidates on the basis of thepositional relationship between the plurality of extracted feature pointcandidates (steps 701 to 707 in FIG. 7). Consequently, points which seemto be certain as those representing the outline of the while line can bedecided as the feature points from among the feature point candidatesextracted from the image.

Second Embodiment

Next, a second embodiment of the present invention will be explained. Inthis embodiment, an explanation will be provided about an example inwhich a division line on the road is recognized by using a plurality ofimages captured in chronological order. Incidentally, the hardwareconfiguration and functional configuration of a division linerecognition apparatus according to this embodiment are respectively thesame as those explained in the first embodiment, so that an explanationabout them has been omitted in the following explanation.

FIG. 15 is a diagram illustrating a processing flow of a division linerecognition apparatus according to a second embodiment of the presentinvention. With the division line recognition apparatus 1 according tothis embodiment, the CPU 104 executes a processing flow illustrated inFIG. 15 following the processing in step 305 in FIG. 3. In thisprocessing flow, pixels which are classified, respectively, as any oneof the “road surface,” the “white line,” and “indefinite” and tend toeasily appear, are identified by executing statistic processing onimages acquired in the past; and whether the classification results ofthe current frame are likely to be certain or not is judged by checkingthe above-identified pixels against the classification results of thecurrent frame.

In step 1501, the statistics calculation unit 202 is caused to find adeviation value of the luminance of each pixel in a plurality of imagesacquired in the past. Under this circumstance, by targeting all framesof a specified distance for which the vehicle equipped with the divisionline recognition apparatus 1 traveled in the past, for example, all theframes of 10 m in the past, the deviation value of the luminance of eachpixel is found for each frame. The deviation value of the luminance ofeach pixel can be calculated by using an average value and a standarddeviation which are calculated from the luminance value of each pixelwithin the processing area which is set in the relevant frame, forexample, according to the following formula.

Deviation Value=(Luminance Value−Average Value)/Standard Deviation

In step 1502, the statistics calculation unit 202 is caused to calculatethe deviation average of each pixel by using the deviation value of theluminance of each pixel, which was found for each frame in step 1501.Under this circumstance, the deviation average of each pixel iscalculated by targeting all the frames of 10 m in the past, regardingwhich the deviation value of the luminance of each pixel was found instep 1501, and averaging the deviation values of the luminance in allthe frames with respect to each pixel.

In step 1503, the statistics calculation unit 202 is caused to normalizethe luminance value of each pixel in the plurality of images acquired inthe past by using the deviation average of each pixel as calculated instep 1502. Under this circumstance, the luminance value of each pixel isnormalized according to the range of values which the luminance value ofeach pixel in all the frames of 10 m in the past can become.Accordingly, for example, the luminance value of each pixel in all theframes of 10 m in the past is normalization within the range from 0 to255. In subsequent processing, the white line threshold value and theroad surface threshold value are set by using the thus-normalizedluminance value of each pixel.

In step 1504, the statistics calculation unit 202 is caused to apply theprocessing area of the current frame to all the frames of 10 m in thepast regarding which the luminance value was normalized in step 1503.Then, an average and standard deviation of the normalized luminancevalue, which was found in step 1503, are found with respect to aplurality of pixels contained in that processing area. Accordingly, thestatistics of the image in the processing area are calculated by usingthe plurality of images respectively captured by the camera 101 atdifferent times of day.

In step 1505, the threshold value setting unit 203 is caused to set thewhite line threshold value and the road surface threshold value by usingthe statistics of the normalized luminance value found in step 1504,that is, the average and the standard deviation.

Under this circumstance, the white line threshold value and the roadsurface threshold value are set with respect to the normalized luminancevalue by a method similar to that explained in the first embodiment onthe basis of the average and the standard deviation of the normalizedluminance value of each pixel.

In step 1506, the division line feature point extraction unit 205 iscaused to perform normalized classification of each pixel in theprocessing area of all the frames of 10 m in the past by using the whiteline threshold value and the road surface threshold value which were setin step 1504. Under this circumstance, the classification unit 205-1 iscaused to classify each pixel into any one of the “road surface,” the“white line,” and “indefinite” by executing processing similar to thosein steps 601 to 606 in FIG. 6 as explained in the first embodiment byusing the normalized luminance value of each pixel in the processingarea, which was found in step 1503.

As a result of the above-described processing in steps 1501 to 1506, theclassification results similar to those of the current frame can beobtained as the normalized classification result of each pixel withrespect to the frames of the specified distance for which the vehicletraveled in the past. In subsequent processing, whether each featurepoint can be trustworthy or not is judged by comparing this normalizedclassification result with the classification result of each featurepoint extracted from the current frame.

In steps 1507 to 1510, the feature point decision unit 205-3 is causedto execute the following processing on each feature point extracted fromthe current frame.

In step 1507, the coordinates and classification of the relevant featurepoint which is a processing object are obtained. Under thiscircumstance, the coordinates and classification of the relevant featurepoint are obtained by acquiring the feature point information finallyobtained by the processing in FIG. 7 from the feature point buffer inFIG. 9 and extracting information corresponding to the feature point,which is the processing object, from that feature point information.

In step 1508, the normalized classification at the same coordinates isobtained on the basis of the coordinates of the feature point in thecurrent frame, which were acquired in step 1507. Under thiscircumstance, the normalized classification result corresponding to thecoordinates of the feature point in the current frame is selected andobtained from among the normalized classification results obtained instep 1506.

In step 1509, the classification of the feature point in the currentframe which was obtained step 1507 is compared with the normalizedclassification obtained in step 1508. As a result, if theseclassifications are different, the information of the relevant featurepoint is deleted from the feature point buffer in step 1510 and then theprocessing returns to step 1507 and proceeds to the processing targetedat the next feature point. On the other hand, if it is determined instep 1509 that these classifications are the same, the processingreturns to step 1507 without doing anything, so that the information ofthe relevant feature point is retained in the feature point buffer andthe processing proceeds to the processing targeted at the next featurepoint.

In steps 1507 to 1510, the classification result of each feature pointis compared with the past frames by executing the above-describedprocessing on each feature point extracted from the current frame.Accordingly, it is possible to recognize which classification tends toeasily appear at each coordinate in the image; and if any feature pointwhich is different from that tendency is extracted from the currentframe, that feature point can be removed as noise.

According to the above-described second embodiment of the presentinvention, the following operational advantage can be further obtainedin addition to the respective operational advantages (1) to (5) asexplained in the first embodiment.

(6) The division line feature point extraction unit 205 classifies theplurality of pixels contained in each of the plurality of imagescaptured by the camera 101 at different times of day; and if the sameclassification result is obtained between the corresponding pixels inthe plurality of images, a feature point of the division line isextracted by using the classification result of such pixels.Specifically speaking, the statistics calculation unit 202 calculatesthe statistics with respect to the plurality of images in the past(steps 1501 to 1504 in FIG. 15). The division line feature pointextraction unit 205 classifies the plurality of pixels contained in theplurality of images in the past on the basis of the statisticscalculated by the statistics calculation unit 202 (step 1506) andcompares the classification results with the classification results ofthe plurality of pixels contained in the image of the current framecaptured by the camera 101 later than the plurality of images in thepast (steps 1507 to 1509). As a result, if the same classificationresult is obtained between the corresponding pixels (step 1509: Yes),the feature point of the division line is extracted by using theclassification result of the relevant pixel by retaining the informationof the relevant pixel without deleting it from the feature point buffer.Consequently, the feature point of the division line can be extractedmore accurately from the current frame by using the images captured inthe past.

Incidentally, the aforementioned embodiments have described the divisionline recognition apparatus 1 which is mounted in the vehicle as anexample; however, the present invention is not limited to this example.For example, the present invention can be also applied to a case wherean image captured by the camera mounted in the vehicle is transmitted toa server and the server receives that image and recognizes the divisionline on the road. In this case, the server which is not mounted in thevehicle serves as the division line recognition apparatus according tothe present invention. Besides this, the present invention can beimplemented by any arbitrary form.

Furthermore, in the above-explained embodiments, the functions of thedivision line recognition apparatus 1 are implemented by execution ofprograms by the CPU 104; however, the present invention is not limitedto this example. For example, some or all the functions of the divisionline recognition apparatus 1 may be implemented by using an FPGA(Field-Programmable Gate Array). Besides this, the division linerecognition apparatus according to the present invention can beimplemented by any arbitrary hardware configuration.

The aforementioned embodiments have explained the example in which eachpixel in the image is classified into any one of the “road surface,” the“white line,” and “indefinite” by using the white line threshold valueand the road surface threshold value; however, the present invention isnot limited to this example. As long as each pixel in the image can beclassified into at least two types, preferably three or more types, anyarbitrary number of threshold values can be set and each pixel in theimage can be classified into the arbitrary number of classificationsaccording to such threshold values.

The above-described embodiments and various kinds of variations aremerely examples. The present invention is not limited to theabove-described embodiments unless they impair the features of thepresent invention; and other aspects which can be thought of within thescope of the technical idea of the present invention are also includedwithin the scope of the present invention.

REFERENCE SIGNS LIST

-   1: division line recognition apparatus-   101: camera-   103: storage apparatus-   104: CPU-   105: memory-   106: CAN I/F-   107: CAN bus-   108: vehicle control unit-   201: processing area setting unit-   202: statistics calculation unit-   203: threshold value setting unit-   204: division line standard data-   205: division line feature point extraction unit-   206: division line decision unit

1. An apparatus for recognizing a division line on a road from an imagecaptured by a camera, the apparatus comprising: a processing areasetting unit configured to set a processing area to the image; astatistics calculation unit configured to calculate statistics of theimage in the processing area; a threshold value setting unit configuredto set a plurality of threshold values on the basis of the statistics; adivision line feature point extraction unit configured to classify aplurality of pixels contained in the image on the basis of the pluralityof threshold values to distinguish between a road surface and a whiteline and extracts a feature point of the division line on the basis ofclassification results of the plurality of pixels; and a division linedecision unit configured to decide the division line on the basis of thefeature point extracted by the division line feature point extractionunit.
 2. The division line recognition apparatus according to claim 1,wherein the division line feature point extraction unit defines thepixels which cannot be classified as either the road surface or thewhite line, as indefinite classification and recognizes the pixels whichare the indefinite classification as the road surface or the white lineaccording to a classification change of the pixels in a scanningdirection of the image.
 3. The division line recognition apparatusaccording to claim 1, wherein the statistics calculation unit definespart or whole of the image as a processing area and calculates anaverage value and a standard deviation of a luminance value of each ofthe pixels in the processing area as the statistics.
 4. The divisionline recognition apparatus according to claim 1, wherein the statisticscalculation unit defines part or whole of the image as a processing areaand calculates an median value and a median absolute deviation of aluminance value of each of the pixels in the processing area as thestatistics.
 5. The division line recognition apparatus according toclaim 1, wherein the division line feature point extraction unitextracts a point corresponding to a boundary at which the classificationof the plurality of pixels changes, as a candidate for the featurepoint.
 6. The division line recognition apparatus according to claim 1,wherein the division line feature point extraction unit extracts aplurality of feature point candidates on the basis of classificationresults of the plurality of pixels and decides the feature point fromthe feature point candidates on the basis of a positional relationshipbetween the plurality of extracted feature point candidates.
 7. Thedivision line recognition apparatus according to claim 1, wherein thedivision line feature point extraction unit classifies a plurality ofpixels contained in a plurality of images captured by the camera atdifferent times of day; and if the same classification result isobtained between corresponding pixels in the plurality of images, thedivision line feature point extraction unit extracts the feature pointof the division line by using the classification result of such pixels.8. The division line recognition apparatus according to claim 7, whereinthe statistics calculation unit calculates the statistics about theplurality of images in the past; and wherein when the division linefeature point extraction unit compares classification results of theplurality of pixels contained in the plurality of images in the past onthe basis of the statistics calculated by the statistics calculationunit with classification results of the plurality of pixels contained inan image captured by the camera later than the plurality of images inthe past and if the same classification result is obtained between thecorresponding pixels, the division line feature point extraction unitextracts the feature point of the division line by using theclassification result of such pixels.